# Fast Record Linkage for Company Entities

## TL;DR

This paper presents a scalable, enterprise-grade system for fast and accurate company entity record linkage, combining rule-based algorithms with machine learning and MinHash for efficient large-scale data integration.

## Contribution

It introduces an end-to-end system that significantly reduces linkage time and improves accuracy for company entity matching using innovative decomposition and scoring methods.

## Key findings

- Achieves 91% recall on real-world datasets
- Scales linearly with system nodes
- Outperforms baseline approaches in accuracy

## Abstract

Record linkage is an essential part of nearly all real-world systems that consume structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and data integration processes often have to be completed before any data analytics and further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights into the data at hand. These insights guide further analytic approaches to the data and support data visualization. In this work we focus on company entity matching, where company name, location and industry are taken into account. Our contribution is an end-to-end, highly scalable, enterprise-grade system that uses rule-based linkage algorithms extended with a machine learning approach to account for short company names. Linkage time is greatly reduced by efficient decomposition of the search space using MinHash. High linkage accuracy is achieved by the proposed thorough scoring process of the matching candidates. Based on real-world ground truth datasets, we show that our approach reaches a recall of 91% compared to 73% for baseline approaches. These results are achieved while scaling linearly with the number of nodes used in the system.